via @errant253

Awk is a text processing language that comes standard with most distributions of Linux/Unix. In my personal experience, awk is faster at parsing, filtering, and writing text files than either python or R with few exceptions. This cheat sheet goes over the basic awk commands that I use the most.

Awk processes a text file line by line and is used to apply some condition to each based on its contents. I have found the most use for it on text files of large matrices (that is text files with distinct, consistent columns) or on text that has clear consistent delimeters. Awk interpretes each column in your line and stores it as a variable from 1 to n where n is the number of columns you have. Say you have a file that looks as such:

ID  gene_name type  start stop Chr
ENSG0 C1orf22 protein_coding 178965 183312 chr2

You write all prose output in Simplified Technical English, adapted from the ASD-STE100 standard (Issue 9). The aerospace industry built this standard so that a reader cannot misread an instruction. Apply the same discipline to everything you write to the user: answers, summaries, status updates, explanations, and instructions.

Ghostty + Zoxide + Yazi + Oh-My-Zsh 完整配置

验证环境（2026-10-28）：macOS 15.6.1、Zsh 5.9、Ghostty 1.4.0、Yazi 26.1.22。Yazi 配置已在本机读取核验。 Zoxide 和 Oh-My-Zsh 未做版本兼容性验证。

| 工具 | 用途 |

AGENTS.md has emerged as the de facto open standard for guiding AI coding assistants, now adopted by over 20,000 repositories and formalized in August 2025 through collaboration between OpenAI, Google, Cursor, Factory, and Sourcegraph. This file acts as a "README for machines"—providing structured, technical context that helps AI assistants write better code from the start. For Python + AWS + Terraform projects, a well-crafted AGENTS.md dramatically reduces friction, ensuring generated code follows your conventions, uses the right tools, and adheres to security requirements.

AGENTS.md is a dedicated Markdown file that complements, not replaces, README.md. While README targets human developers with project overviews and quick-start guides, AGENTS.md contains detailed technical instructions specifically for AI coding agents. Think of it as onboarding documentation for an AI team member: ex

Date: 2026-05-05
Author: Peter (Synchronic1) / Carlsson Creative
Host: VM104 (192.168.1.243), Proxmox VM on Dell PowerEdge T630
llama.cpp Version: 8240 (d088d5b74)

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.